DNN 与 SOTIF 论文。

简介：
This paper delves into the application of Deep Neural Networks (DNN) within the framework of Safety Of The Intended Functionality (SOTIF). It explores how DNNs can be leveraged to enhance the reliability and safety of autonomous systems, addressing potential hazards and ensuring predictable behavior throughout the vehicles operational lifecycle. The research investigates techniques for validating and verifying DNN-based systems, particularly focusing on scenarios that may not be explicitly covered during traditional testing. Furthermore, the paper examines methodologies for mitigating risks associated with unforeseen circumstances and ensuring robust performance under challenging conditions. It presents a comprehensive overview of current advancements and challenges in integrating DNNs with SOTIF principles, contributing to safer and more dependable autonomous driving technologies.